Radiation therapy planning utilizes knowledge of the properties of radiation beams in tissue, and relies on calculation of radiation dose in the tumor region and in other locations of the patient's body. Manual optimization of treatment plans for an individual patient based on dose computations with variation of parameters can only consider a few of the possible options. We have developed an expert system for planning radiation therapy as an alternative approach. The proposed work will extend this system to address several research topics in the area of knowledge-based systems for planning therapy that have arisen out of the previous developments. The search space of radiation treatment plans has both discrete and continuous dimensions, representing dozens to hundreds of adjustable parameters in a typical treatment plan. The solution space is therefore extremely large. Radiation dose calculation is necessary for each plan the search considers, in order to evaluate the proposed plan. This computation is clearly the rate limiting step. Therefore it is important to investigate how the efficiency of search for a solution to a planning problem can be increased by modifying the search strategy. The expert system we have developed will be used to achieve two main objectives: 1. to collect data on search strategies for performing optimization in a complex planning domain, in which we can vary the parameters of the search algorithm systematically to see if there are optimal answers to trade-offs between e.g., depth-first or breadth-first, pruning algorithms, broad search or fine tuning of plans, and other issues. 2. to systematically explore the possibilities for configuring radiation beams for different classes of tumor sites, which would likely lead to unusual but more effective treatment configurations than are normally considered in clinical practice. This offers the potential to learn how to dramatically improve the accuracy and effectiveness of radiation treatment and thereby increase the cure rate for many cancers. Other issues to be investigated include: how graphical tools can aid in the above, i.e., tools for visualization of large data sets; how the user interface can be built to smoothly integrate the automated search process into the clinical work flow; how to integrate the system with other information sources (e.g., an anatomy browser), and how to verify the correctness of the program as a whole.